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基于近紅外顯微成像的豆粕和抗生素菌渣鑒別分析
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中國(guó)農(nóng)業(yè)科學(xué)院基本科研業(yè)務(wù)費(fèi)專項(xiàng)(1610072017001)和中國(guó)農(nóng)業(yè)科學(xué)院“飼料質(zhì)量安全檢測(cè)與評(píng)價(jià)”創(chuàng)新團(tuán)隊(duì)經(jīng)費(fèi)項(xiàng)目


Identification and Analysis of Soybean Meal and Antibiotic Mycelial Residues Based on Near Infrared Micro-imaging
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    摘要:

    以豆粕和3種抗生素菌渣為研究對(duì)象,,通過傅里葉變換近紅外顯微成像系統(tǒng)采集樣品近紅外顯微圖像,;對(duì)采集到的近紅外顯微圖像進(jìn)行光譜重構(gòu),并對(duì)所有樣品光譜進(jìn)行預(yù)處理,,利用Duplex算法分別從不同的樣品預(yù)處理光譜中篩選具有代表性的光譜建立豆粕和抗生素菌渣的特征光譜庫(kù),。使用偏最小二乘判別分析(PLS-DA)與支持向量機(jī)判別分析(SVM-DA)結(jié)合不同的光譜預(yù)處理方法,構(gòu)建豆粕與不同種類抗生素菌渣的近紅外顯微成像定性判別模型,。結(jié)果表明:構(gòu)建的2種模型均能有效對(duì)試驗(yàn)中所用豆粕和抗生素菌渣樣品進(jìn)行鑒別分析,,正確率均在99.4%以上。進(jìn)一步比較研究發(fā)現(xiàn),,一階導(dǎo)數(shù)+SNV的預(yù)處理方式優(yōu)于無預(yù)處理,、一階導(dǎo)數(shù)、二階導(dǎo)數(shù),;SVM-DA的模型效果優(yōu)于PLS-DA,,SVM-DA中特征提取方法PLS優(yōu)于PCA。

    Abstract:

    AMR (antibiotic mycelial residue) added to animal feed easily leads to drug resistance influencing human health and environment. However, there is a lack of effective detection methods, especially fast and convenient detection technology, to distinguish AMR from animal feed. In order to search effective detection methods, qualitative discriminant analysis of soybean meal and antibiotic residue was made at first. The feasibility of near infrared micro-imaging for the identification of soybean meal and antibiotic mycelial residues was explored. Three soybean meal samples and three kinds of antibiotic mycelial residues were used to collect the near-infrared microscopic images of the samples by Fourier transform near-infrared microscopy. The near-infrared microscopic images collected were reconstructed and the spectra of all the samples were pretreated. The Duplex algorithm was employed to screen the representative spectra from pretreatment spectra of different samples to establish spectral library of soybean meal and antibiotic mycelial residues. Different discriminant models of soybean meal and different kinds of antibiotic mycelial residues were built by using different pretreatment methods combined with PLS-DA(partial least squares discriminant analysis)and SVM-DA (support vector machine discriminant analysis). The results showed that two kinds of modeling methods based on near-infrared micro-imaging spectroscopy were effective in the identification of three kinds of antibiotic mycelial residues and soybean meal samples, and the correctness rate was above 99.4%. The first-order derivative + SNV preprocessing method was better than that without preprocessing, the first derivative and the second derivative. SVM-DA model was superior to PLS-DA, and SVM-DA in feature extraction method was better than PCA (principal component analysis). The results presented indicated that the near infrared microscopic imaging technique can be used to qualitatively distinguish antibiotic mycelial residue from soybean meal, and it also provided theoretical basis for further research.

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楊增玲,林玉飛,梁浩,李守學(xué),肖志明,樊霞.基于近紅外顯微成像的豆粕和抗生素菌渣鑒別分析[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(12):363-369. YANG Zengling, LIN Yufei, LIANG Hao, LI Shouxue, XIAO Zhiming, FAN Xia. Identification and Analysis of Soybean Meal and Antibiotic Mycelial Residues Based on Near Infrared Micro-imaging[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(12):363-369.

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  • 收稿日期:2017-07-25
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  • 在線發(fā)布日期: 2017-12-10
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